Neural Network Multiphase Fluid Flow Equilibrium Modeling
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Solution Overview
Problem
Current methods for modeling multiphase fluid flow in porous media are computationally intensive and time-consuming, particularly due to the need for frequent thermodynamic and geochemical equilibrium calculations, which dominate the simulation time and resource requirements.
Innovation Solution
Implementing neural networks to determine thermodynamic and geochemical equilibria, replacing traditional equation-solving methods, allowing for faster and more efficient calculation of fluid properties and compositions at equilibrium.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional equation-solving methods are used to determine thermodynamic and geochemical equilibria, then accuracy in modeling fluid properties is maintained, but computational time and resources are significantly increased
Solution Approach 1:
The patent applies preliminary action by pre-training neural networks offline using extensive thermodynamic and geochemical equilibrium data. This training phase is performed beforehand, allowing the trained models to rapidly predict equilibrium properties during actual simulations without performing computationally intensive calculations at runtime. The neural networks are prepared in advance to handle the complex equilibrium determinations efficiently during production use.
Solution Approach 2:
The patent substitutes the traditional mechanical equation-solving approach with a neural network-based predictive system. Instead of iteratively solving complex thermodynamic and geochemical equilibrium equations during simulation, the system uses pre-trained neural networks that have learned the equilibrium relationships from training data, replacing the computational mechanics with a faster artificial intelligence-based prediction mechanism.
2Measurement precision
If traditional equation-solving methods are used to determine thermodynamic and geochemical equilibria, then accurate fluid composition calculations are achieved, but computational resources and simulation time are significantly increased
Solution Approach 1:
The patent applies preliminary action by pre-training neural networks offline using extensive thermodynamic and geochemical equilibrium data. This training phase is performed beforehand, allowing the trained models to rapidly predict equilibrium properties during actual simulations without performing computationally intensive calculations at runtime. The neural networks are prepared in advance to handle the complex equilibrium determinations efficiently during production use.
Solution Approach 2:
The patent substitutes the traditional mechanical equation-solving approach with a neural network-based predictive system. Instead of iteratively solving complex thermodynamic and geochemical equilibrium equations during simulation, the system uses pre-trained neural networks that have learned the equilibrium relationships from training data, replacing the computational mechanics with a faster artificial intelligence-based prediction mechanism.
3Reliability
If complex equilibrium equations are solved at each time step and mesh, then accurate multiphase fluid flow modeling is achieved, but computational complexity and time requirements increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-training neural networks offline using extensive thermodynamic and geochemical equilibrium data. This training phase is performed beforehand, allowing the trained models to rapidly predict equilibrium properties during actual simulations without performing computationally intensive calculations at runtime. The neural networks are prepared in advance to handle the complex equilibrium determinations efficiently during production use.
Solution Approach 2:
The patent substitutes the traditional mechanical equation-solving approach with a neural network-based predictive system. Instead of iteratively solving complex thermodynamic and geochemical equilibrium equations during simulation, the system uses pre-trained neural networks that have learned the equilibrium relationships from training data, replacing the computational mechanics with a faster artificial intelligence-based prediction mechanism.
Data Source
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AI summary
The invention relates to a method for dynamically determining at least one physicochemical property and the composition of a multiphase fluid flow in a porous medium, including: constructing a geological model of said porous medium and discretizing the model into basic cells; determining (40) an initial status of each cell, including determining the composition of said cell; and determining (42) a change, in at least one cell and during at least one predetermined time interval, in at least one physicochemical property and in the phase composition of said fluid in each component, comprising at least one step (52, 54) of determining an equilibrium within said cell, including the use of at least one neuronal network to determine characteristics that are in equilibrium in said fluid from the characteristics thereof that are out of equilibrium.